1. Field
The present disclosure relates generally to point clouds and, in particular, to increasing the resolution of point clouds. Still more particularly, the present disclosure relates to a system and method for increasing the number of points in a point cloud to increase the resolution of the point cloud.
2. Background
A point cloud is a collection of points in a three-dimensional (3D) coordinate system that describe a three-dimensional scene. Typically, the points in a point cloud represent external surfaces of objects. A light detection and ranging (LIDAR) system is an example of one type of sensor system capable of generating a point cloud. Point clouds may also be generated using, for example, stereo camera systems, mobile laser imaging systems, and other types of sensor systems.
Point clouds may be used for performing various operations such as, for example, object identification, object classification, scene visualization, segmentation, two-dimensional image data enhancement, and/or other types of operations. The level of performance with which these operations are performed using a point cloud may depend on the resolution of that point cloud.
As used herein, the “resolution” of a point cloud may be the level of detail with which features in the scene captured by the point cloud may be discernible within the point cloud. The resolution of a point cloud may depend on the number of points in the point cloud and/or the point density of the points in one or more portions of the point cloud. As used herein, “point density” is a measure of the number of points per unit volume. A portion of a point cloud having a higher density than another portion of the point cloud may be less sparse than the other portion.
In some situations, object identification, object classification, segmentation, and/or visualization of a scene using a sparse point cloud may yield inaccurate results. For example, a point cloud may be insufficiently dense to correctly identify or classify an object.
Some currently available solutions for increasing the number of points in a point cloud include making assumptions about the objects in the scene. For example, assumptions may be made about the shape of an object in the scene and new points may be added to the point cloud based on those assumptions. However, with these types of solutions, the locations in the three-dimensional reference coordinate system at which the new points are added may be less accurate than desired.
Further, some currently available solutions may be unable to account for actual holes or gaps in a scene. For example, with some currently available solutions, new points may be added to a point cloud at locations that represent actual holes or gaps in the scene. Still further, some currently available solutions may add points to a point cloud that connect objects that are unconnected in the scene, such as, for example, a tree top and the ground. Therefore, it would be desirable to have a method and apparatus that takes into account at least some of the issues discussed above, as well as other possible issues.